Positional Accuracy Measurement and Error Modeling for Mobile Tracking

Sharp, Ian; Yu, Kegen; Sathyan, T.

doi:10.1109/tmc.2011.119

Cited by 31 publications

(15 citation statements)

References 19 publications

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“…(25), and the equivalent FIM J xt BE associated with x t retains all necessary information for the target tracking, in terms of [41]. Based on the matrix partition in Eq.…”

Section: ) Bayesian Fimmentioning

confidence: 99%

“…Considering the measurement accuracies at reference nodes are random and nondeterministic in the spatial-temporal domain, in this paper, we introduce an independent and identical Wishart density, which is the conjugate priori of the precision of a Gaussian variable [22], [23], to characterize the statistical dynamics and randomness of measurement accuracies at reference nodes. In addition, the Gaussian density is employed to characterize the initial location errors of sensor nodes [24], [25]. The associated Cramer-Rao lower bound (CRLB) is utilized to disclose both the mobile target tracking EP and the sensor node location calibration EP.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Error Propagation Analysis of the Received Signal Strength-Based Simultaneous Localization and Tracking in Wireless Sensor Networks

Zhou

Chen

Xiao

2017

IEEE Trans. Inform. Theory

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Abstract-Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, a localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatialtemporal-domain doubly random due to the target mobility, environment dynamics and different surroundings at different reference nodes. Firstly, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Secondly, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Thirdly, the convergence conditions, convergence properties and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme. Finally, numerical simulations are presented to corroborate the EP analysis.

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“…(25), and the equivalent FIM J xt BE associated with x t retains all necessary information for the target tracking, in terms of [41]. Based on the matrix partition in Eq.…”

Section: ) Bayesian Fimmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Error Propagation Analysis of the Received Signal Strength-Based Simultaneous Localization and Tracking in Wireless Sensor Networks

Zhou

Chen

Xiao

2017

IEEE Trans. Inform. Theory

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“…However, the demand to reduce hardware dependency and energy cost has been the focus of academia and industry, and some researchers also proposed a range-free localization algorithm [10]. Usually, range-free location algorithms demonstrate poor performance in the aspect of positioning accuracy than the range-based localization algorithm, but it does not need additional hardware support and can meet many requirements in the scenarios with rough localization effect.…”

Section: Related Workmentioning

confidence: 99%

“…Usually, range-free location algorithms demonstrate poor performance in the aspect of positioning accuracy than the range-based localization algorithm, but it does not need additional hardware support and can meet many requirements in the scenarios with rough localization effect. In [10], an indoor localization strategy for mini-UAV in the presence of obstacles is proposed, in which the signal propagation state is identified according to the prior probability and statistics of TDOA and RSS measurements. In [11], a NLOS identification and weaken algorithm with machine learning is proposed to identify and weaken the NLOS error by means of support vector machine (SVM), which can employ a large number of data samples to train the SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao

Zhu

2018

J Wireless Com Network

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The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node's localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithmprovidesabetterpositionestimationwithlesslocalizationerror.

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“…where we assume node location precision U i is independent to others, since the measurements and location estimations of different nodes are independent from each other [22], [23]. The location uncertainty is defined as the inverse of the location precision matrix U i .…”

Section: A Network Modelmentioning

confidence: 99%

On the Spatial Error Propagation Characteristics of Cooperative Localization in Wireless Networks

Zhou

Chen

Xiao

et al. 2017

IEEE Trans. Veh. Technol.

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Abstract-Cooperative localization is an important technique in wireless networks. However, there are always errors in network node localization, which will spatially propagate among network nodes when performing network localization. In this paper, we study the spatial error propagation characteristics of network localization, in terms of Fisher information. Firstly, the spatial propagation function is proposed to reveal the spatial cooperation principle of network localization. Secondly, the convergence property of spatial localization information propagation is analyzed to shed light on the performance limits of network localization through spatial information propagation. It is shown that, (ı) the network localization error propagates in a way of the Ohm's law in electric circuit theory, where the measurement accuracy, node location accuracy and geometric-resolution information behave like the resistances connected in parallel or series; (ıı) the network location error gradually diminishes with spatial localization cooperation procedures, due to the cooperative localization information propagation; (ııı) the essence of spatial localization cooperation among network nodes is the spatial propagation of localization information.

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Positional Accuracy Measurement and Error Modeling for Mobile Tracking

Cited by 31 publications

References 19 publications

The Error Propagation Analysis of the Received Signal Strength-Based Simultaneous Localization and Tracking in Wireless Sensor Networks

The Error Propagation Analysis of the Received Signal Strength-Based Simultaneous Localization and Tracking in Wireless Sensor Networks

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

On the Spatial Error Propagation Characteristics of Cooperative Localization in Wireless Networks

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